Failure prediction signaling and cognitive user migration

ABSTRACT

Failure prediction signaling and cognitive user migration may be provided. A client device may receive at least a portion of failure prediction data. The client device may then analyze the at least the portion of the failure prediction data. The client device may then roam from a first computing device to a second computing device in response to analyzing the at least the portion of the failure prediction data.

TECHNICAL FIELD

The present disclosure relates generally to failure prediction andcognitive user migration.

BACKGROUND

In computer networking, a wireless Access Point (AP) is a networkinghardware device that allows a Wi-Fi compatible client device to connectto a wired network and to other client devices. The AP usually connectsto a router (directly or indirectly via a wired network) as a standalonedevice, but it can also be an integral component of the router itself.Several APs may also work in coordination, either through direct wiredor wireless connections, or through a central system, commonly called aWireless Local Area Network (WLAN) controller. An AP is differentiatedfrom a hotspot, which is the physical location where Wi-Fi access to aWLAN is available.

Prior to wireless networks, setting up a computer network in a business,home, or school often required running many cables through walls andceilings in order to deliver network access to all of thenetwork-enabled devices in the building. With the creation of thewireless AP, network users are able to add devices that access thenetwork with few or no cables. An AP connects to a wired network, thenprovides radio frequency links for other radio devices to reach thatwired network. Most APs support the connection of multiple wirelessdevices. APs are built to support a standard for sending and receivingdata using these radio frequencies.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various embodiments of the presentdisclosure. In the drawings:

FIG. 1 is a block diagram of an operating environment for providingfailure prediction signaling and cognitive user migration;

FIG. 2 is a flow chart of a method for providing failure predictionsignaling and cognitive user migration;

FIG. 3 is a flow diagram of a method for providing failure predictionsignaling and cognitive user migration;

FIG. 4 is a flow chart of a method for providing failure predictionsignaling and cognitive user migration;

FIG. 5 is a flow diagram of a method for providing failure predictionsignaling and cognitive user migration; and

FIG. 6 is a block diagram of a computing device.

DETAILED DESCRIPTION Overview

Failure prediction signaling and cognitive user migration may beprovided. A client device may receive at least a portion of failureprediction data. The client device may then analyze the at least theportion of the failure prediction data. The client device may then roamfrom a first computing device to a second computing device in responseto analyzing the at least the portion of the failure prediction data.

Both the foregoing overview and the following example embodiments areexamples and explanatory only, and should not be considered to restrictthe disclosure's scope, as described and claimed. Furthermore, featuresand/or variations may be provided in addition to those described. Forexample, embodiments of the disclosure may be directed to variousfeature combinations and sub-combinations described in the exampleembodiments.

Example Embodiments

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar elements.While embodiments of the disclosure may be described, modifications,adaptations, and other implementations are possible. For example,substitutions, additions, or modifications may be made to the elementsillustrated in the drawings, and the methods described herein may bemodified by substituting, reordering, or adding stages to the disclosedmethods. Accordingly, the following detailed description does not limitthe disclosure. Instead, the proper scope of the disclosure is definedby the appended claims.

High throughput wireless networking may comprise a business-criticaltool. However, any failure in the wireless infrastructure (e.g., aWireless Local Area Network controller (WLC) or an Access Point (AP)failure) may consume convergence time, and may be directly influenced bythe time taken to detect the failure and react. As the wirelessbandwidth goes up, the percentage of traffic loss similarly increases.For example, some wireless standards may offer 30 Gbps of bandwidth. Assuch, if a radio with 10 connected users fails, it may result in around300+ Gbps of data loss (assuming the failure can be detected and theusers may be reconnected to another AP within, for example, a second).Detection mechanisms, however, may be associated with rate shifting andretry logics. For example, a link evaluation algorithm may retry threetimes at a current rate (e.g., Modulation and Coding Scheme (MCS) 5),then rate shift from MCS 5 to MCS 4 for two retries, then jump to MCS 2for one attempt, then to MCS 0 before deciding to panic scan. This maycause a total time cost, from AP failure to the scanning decision, ofgreater than two seconds without accounting for the scan/reassociationtime.

Artificial Intelligence (AI) for networking has been introduced andvarious models have been proposed and used for failure prediction. Inone study analyzing datacenter logs, it was noticed that 95% of eventsmay happen within 400 ms of each other. Various models may also predictan event with a timer variant of around one or two seconds before theactual event happens. Some failure prediction tools may successfullypredict WLC and AP failures in many scenarios. Yet the outcome may belimited to surfacing an alarm in an Information Technology (IT)administration management tool (e.g., a Digital Network ArchitectureCenter (DNAC) controller), with a root cause once the issue hasoccurred. The effect of the failure, however, may still be the same forclient devices on the wireless network. Note that failures may notnecessarily be crashes. They may be crashes, but they may also becritical Over-the-Air (OTA) collision/retry levels, high ChannelUtilization (CU), etc.

Embodiments of the disclosure may leverage such predictiveness behaviorand extend the wireless control plane signaling mechanisms to signal thedetails. As such, predicted failure, the accuracy of the failure, and apotential AP list to which the client device may re-connect to may besignaled. In addition, embodiments of the disclosure may take proactiveaction at the infrastructure level, for example, signaling to the WLC animminent AP failure, causing the WLC to increase the neighboring APpower.

FIG. 1 shows an operating environment 100 for providing failureprediction signaling and cognitive user migration. As shown in FIG. 1,operating environment 100 may comprise a controller 105 and a coverageenvironment 110. Coverage environment 110 may comprise, but is notlimited to, a Wireless Local Area Network (WLAN) comprising a pluralityof Access Points (APs) that may provide wireless network access (e.g.,access to the WLAN for client devices). The plurality of APs maycomprise a first AP 115, a second AP 120, a third AP 125, a fourth AP130, and a fifth AP 135. The plurality of APs may provide wirelessnetwork access to a plurality of client devices as they move withincoverage environment 110. The plurality of client devices may comprise,but are not limited to, a first client device 140, a second clientdevice 145, and a third client device 150. Ones of the plurality ofclient devices may comprise, but is not limited to, a smart phone, apersonal computer, a tablet device, a mobile device, a telephone, aremote control device, a set-top box, a digital video recorder, anInternet-of-Things (IoT) device, a network computer, a router, or othersimilar microcomputer-based device. Each of the plurality of APs may becompatible with specification standards such as, but not limited to, theInstitute of Electrical and Electronics Engineers (IEEE) 802.11axspecification standard for example.

Controller 105 may comprise a Wireless Local Area Network controller(WLC) and may provision and control coverage environment 110 (e.g., aWLAN). Controller 105 may allow first client device 140, second clientdevice 145, and third client device 150 to join coverage environment110. In some embodiments of the disclosure, controller 105 may beimplemented by a Digital Network Architecture Center (DNAC) controller(i.e., a Software-Defined Network (SDN) controller) that may configureinformation for coverage environment 110 in order to provide failureprediction signaling and cognitive user migration. Controller 105 mayalso include a prediction system as described in greater detail below.In some embodiments the prediction system may comprise a standalonesystem.

The prediction system may apply processes that leverage Machine Learning(ML) models that may predict various degrees of failures. The failuresmay be related to the AP host itself (e.g., AP crash or radio freeze) orrelated to the AP services (e.g., high CU, buffer overrun, excessiveclient device count, etc.).

While embodiments of the disclosure may not be limited to a particularML model, ML-based prediction may be probabilistic in nature. Forexample, as the contributing factors increase in number and individualoccurrences, the probability of an event to occur, from low, soonbecomes exponentially probable. Probability reaches 1 as the eventoccurs. However, it may be common to observe a point of probabilityincrease acceleration (i.e., an elbow of no return). Passed that point,unless contributing events completely cease to occur, the only unknownmay be the exact interval at which the individual contributing factorswill reoccur, before the even happens. Once the elbow point is reached,the choice of the implementer is a decision based on a compromisebetween the probability value and the delay before probability reaches 1(i.e., do we surface the alarm at p=0.7 and likely 800 ms before theexpect reach of p=1, or do we wait a few more contributing events for pto reach 0.85 and p=1 within 200 ms).

The aforementioned decision process may be performed by the predictionsystem. As such, past a certain p value, the event may only be likely“not” to happen if the contributing factors suddenly cease to bepresent. The prediction system may surface this probability, and even ifthe implementer decides on a high p level for the alarm, the alarm andthe counteractions described herein may occur before the event (i.e., afailure). Thus, consistent with embodiments of the disclosure, theprediction system may use the prediction/accuracy of the failures andinject this into a cognitive decision process as described below. Thecognitive decision processes may comprise, but are not limited to: i) acognitive user approach—the APs may be instructed with prediction andsignaling, and may let the user (or the client device, automatically)take the cognitive decision of switching to a more suitable AP (e.g.,user controlled switchover); ii) a cognitive AP approach—the APs may beinstructed with prediction, take the cognitive decision, and signal thesame to the users (e.g., backward compatibility); iii) a hybridapproach—this may comprise a hybrid mode where the AP includes itssuggestion based on its own analysis and lets the user make a decisionby considering the signaled details along with the details not availableto the AP (e.g., user centric policy); and iv) a cognitive mitigationapproach—neighboring APs and WLCs may be instructed with failureprediction and apply a what if counteractive process to limit the effectof the failure.

The elements described above of operating environment 100 (e.g.,controller 105, first AP 115, second AP 120, third AP 125, fourth AP130, fifth AP 135, first client device 140, second client device 145, orthird client device 150) may be practiced in hardware and/or in software(including firmware, resident software, micro-code, etc.) or in anyother circuits or systems. The elements of operating environment 100 maybe practiced in electrical circuits comprising discrete electronicelements, packaged or integrated electronic chips containing logicgates, a circuit utilizing a microprocessor, or on a single chipcontaining electronic elements or microprocessors. Furthermore, theelements of operating environment 100 may also be practiced using othertechnologies capable of performing logical operations such as, forexample, AND, OR, and NOT, including but not limited to, mechanical,optical, fluidic, and quantum technologies. As described in greaterdetail below with respect to FIG. 6, the elements of operatingenvironment 100 may be practiced in a computing device 600.

Cognitive User Approach

FIG. 2 is a flow chart setting forth the general stages involved in amethod 200 consistent with embodiments of the disclosure for providingfailure prediction signaling and cognitive user migration. Method 200may be implemented using elements of operating environment 100 (e.g.,first AP 115, first client device 140, etc.) as described in more detailabove with respect to FIG. 1. Flow diagram 300, as shown in FIG. 3, isalso used to illustrate method 200. Ways to implement the stages ofmethod 200 will be described in greater detail below.

Method 200 may begin at starting block 205 and proceed to stage 210where first AP 115 (e.g., a first computing device) may receive failureprediction data. For example, in this cognitive user approach, an AP anda client device may signal support for predictive failure signaling. Asshown in FIG. 3, first client device 140 may attached to first AP 115 incoverage environment 110 (stages 305 and 310 of FIG. 3). First AP 115may receive the failure prediction data indicating a failure predictionfrom the prediction system. The failure prediction data may comprise,but is not limited to, a type of a predicted failure, a probability ofthe predicted failure, a time span until the predicted failure, and adegree of the predicted failure.

From stage 210, where first AP 115 receives the failure prediction data,method 200 may advance to stage 220 where first AP 115 (e.g., firstcomputing device) may send, in response to receiving the failureprediction data, at least a portion of the failure prediction data tofirst client device 140 (stage 315 of FIG. 3). For example, the portionof the failure prediction data may indicate that a failure of first AP115 (e.g., the first computing device) may be likely to occur within apredetermined threshold.

Once first AP 115 sends, in response to receiving the failure predictiondata, at least the portion of the failure prediction data to firstclient device 140 in stage 220, method 200 may continue to stage 230where first client device 140 may analyze at least the portion of thefailure prediction data. For example, first AP 115 may transparentlysignal client device 140 (e.g., an Access Network Query Protocol (ANQP)messages or an 802.11v extension) a failure prediction when thelikelihood reaches a configurable threshold (e.g., first AP 115 crash82.7% likely within 200 ms, first AP 115 resource exhaustion 76.4%likely within 800 ms, etc.). First client device 140 may analyze thisinformation.

After first client device 140 analyzes at least the portion of thefailure prediction data in stage 230, method 200 may proceed to stage240 where first client device 140 may roam from first AP 115 to secondAP 120 (e.g., a second computing device) in response to analyzing atleast the portion of the failure prediction data. For example, theanalysis of this information may nudge first client device 140 to roam(i.e., move) to a better AP (e.g., second AP 120). This roaming my occurbefore first AP 115 fails. Consistent with embodiments of thedisclosure, this movement may not be because of Received Signal StrengthIndicator (RSSI)/roaming logic, but rather because of the predictedfailure. The first client device 140 may still own the final decision toexecute the roam.

In addition, first client device 140 may perform proactive actionsubsequent to analyzing and prior to roaming. The proactive action maycomprise, but is not limited to, one or more of emptying first clientdevice 140's buffer, switching to a blocks Modulation and Coding Scheme(MCS), switching to a more robust MCS, starting a backup Multi-LinkDevice (MLD) link, and trigging a network-layer multipath link. Oncefirst client device 140 roams from first AP 115 to second AP 120 inresponse to analyzing at least the portion of the failure predictiondata in stage 240, method 200 may then end at stage 250.

Cognitive AP Approach

FIG. 4 is a flow chart setting forth the general stages involved in amethod 400 consistent with embodiments of the disclosure for providingfailure prediction signaling and cognitive user migration. Method 400may be implemented using elements of operating environment 100 (e.g.,first AP 115, first client device 140, etc.) as described in more detailabove with respect to FIG. 1. Flow diagram 500, as shown in FIG. 5, isalso used to illustrate method 400. Ways to implement the stages ofmethod 400 will be described in greater detail below.

Method 400 may begin at starting block 405 and proceed to stage 410where first AP 115 (e.g., a computing device) may receive failureprediction data. For example, the AP and the client device may or maynot signal support for predictive failure signaling. While support maybe expected for the cognitive user approach (as described above withrespect to FIG. 2), support may not be required for the cognitive APapproach. As shown in FIG. 5, first client device 140 may attached tofirst AP 115 in coverage environment 110 (stages 505 and 510 of FIG. 5).The likely-to-fail-AP (e.g., first AP 115) may be instructed by theprediction system with the failure prediction (i.e., the failureprediction data).

From stage 410, where first AP 115 receives the failure prediction data,method 400 may advance to stage 420 where first AP 115 may analyze thefailure prediction data. For example, analyzing the failure predictiondata may comprise determining that a probability that first AP 115 mayfail is greater than a predetermined threshold.

Once first AP 115 analyzes the failure prediction data in stage 420,method 400 may continue to stage 430 where first AP 115 may send, inresponse to analyzing the failure prediction data, a message to ones ofthe plurality of client devices to roam away from first AP 115. Forexample, first AP 115 may proactively instruct client devices (e.g.,first client device 140) to move to a neighboring AP (e.g., second AP120). First AP 115 may leverage the predicted failure, accuracy of thefailure, and other user-specific policies to make a decision aboutmoving client user devices to other APs.

For example, when the accuracy is 98%+, the chances of first AP 115going down may be almost certain, so first AP 115 send a message (e.g.,an 802.11v message) (stage 515 of FIG. 5) to all end users, requesting amove to a different AP (e.g., with will disconnect bit set and aninterval lesser than the predicted failure time) (stage 520 of FIG. 5).In another embodiment, messages may be sent to client devices in order,by type (based on client category, traffic criticality etc.) so as tomake sure that critical client devices survive. In another embodiment,first AP 115 action may be driven by the failure prediction. Forexample, if the accuracy is around 70%, first access point 115 mayselectively instruct high profile, highly critical user devices to moveto other access points, while leaving the other low critical userdevices to react after the failure (if it happens). Once first AP 115sends, in response to analyzing the failure prediction data, the messageto the ones of the plurality of client devices to roam away from firstAP 115 in stage 430, method 400 may then end at stage 440.

Hybrid Approach

Consistent with embodiments of the disclosure, first AP 115 may receivefailure prediction data from the prediction system and may send it tofirst client device 140. First client device 140 may receive informationcomprising at least a portion of the failure prediction data, asuggested action, and comparative elements on neighboring computingdevices. First client device 140 may analyze the information and take anaction in response to analyzing the information. In some embodiments theaction may comprises roaming, by first client device 140, from first AP115 to second AP 120 comprising a neighboring AP. In other embodiments,the action may comprise switching, by first client device 140, to one ofa Multi-Link Device (MLD) link, a 4G link, and a 5G link.

In this embodiment, the AP and the client device may signal support ofpredictive failure signaling. For example, first AP 115 may signal(e.g., an ANQP message or an 802.11v extension) the failure predictionwhen the likelihood reaches a (configurable) threshold, along withfailure type and probability. First AP 115 may also provide suggestedactions (e.g., 11v roam), along with comparative elements on neighboringAPs (e.g., roam to second AP 120 suggested, CU 55%, retry 21%, or roamto third AP 125 suggested, CU 37%, retry 23%). Client device 140 mayleverage details received via 802.11v from first AP 115 and use localpolicies to identify if it should move to a different AP or take otheraction (e.g. switch some traffic to MLD link or LTE, reduce/change codecetc.).

Cognitive Mitigation Approach

Consistent with embodiments of the disclosure, a computing device (e.g.,controller 105) may receive failure prediction data from the predictionsystem. The computing device may analyze a configuration of a wirelessnetwork (e.g., coverage environment 110) taking into account the failureprediction data. The computing device may then reconfigure, in responseto analyzing the configuration of the wireless network, the wirelessnetwork. In some embodiments reconfiguring the wireless network maycomprise removing an AP from the wireless network when analyzing theconfiguration of the wireless network taking into account the failureprediction data indicates the AP is likely to fail. In other embodimentsreconfiguring the wireless network may comprise, when analyzing theconfiguration of the wireless network taking into account the failureprediction data indicates a Channel Utilization (CU) on AP is greaterthan a predetermined threshold, increasing client count tolerance forthe wireless network or causing the AP on the wireless network to refusenew associations.

In other words, the prediction system may signal to controller105/neighboring APs the failure prediction for target APs. Controller105/neighboring APs may run what-if mitigation scenarios to proactivelycorrect the network configuration while accounting for the failure(e.g., if first AP 115 will crash, Radio Resource Management (RRM) maybe triggered and may run with first AP 115 removed from the mix; iffirst AP 115 CU to exceed 60%, Overlapping Basic Service Set (OBSS) planmay be re-evaluated, tolerance to client count increased while first AP115 starts refusing new associations, etc.).

FIG. 6 shows computing device 600. As shown in FIG. 6, computing device600 may include a processing unit 610 and a memory unit 615. Memory unit615 may include a software module 620 and a database 625. Whileexecuting on processing unit 610, software module 620 may perform, forexample, processes for providing failure prediction signaling andcognitive user migration as described above with respect to FIG. 2, FIG.3, FIG. 4, and FIG. 5. Computing device 600, for example, may provide anoperating environment for controller 105, first AP 115, second AP 120,third AP 125, fourth AP 130, fifth AP 135, first client device 140,second client device 145, or third client device 150. Controller 105,first AP 115, second AP 120, third AP 125, fourth AP 130, fifth AP 135,first client device 140, second client device 145, or third clientdevice 150 may operate in other environments and are not limited tocomputing device 600.

Computing device 600 may be implemented using a Wi-Fi access point, atablet device, a mobile device, a smart phone, a telephone, a remotecontrol device, a set-top box, a digital video recorder, a cable modem,a personal computer, a network computer, a mainframe, a router, aswitch, a server cluster, a smart TV-like device, a network storagedevice, a network relay devices, or other similar microcomputer-baseddevice. Computing device 600 may comprise any computer operatingenvironment, such as hand-held devices, multiprocessor systems,microprocessor-based or programmable sender electronic devices,minicomputers, mainframe computers, and the like. Computing device 600may also be practiced in distributed computing environments where tasksare performed by remote processing devices. The aforementioned systemsand devices are examples and computing device 600 may comprise othersystems or devices.

Embodiments of the disclosure, for example, may be implemented as acomputer process (method), a computing system, or as an article ofmanufacture, such as a computer program product or computer readablemedia. The computer program product may be a computer storage mediareadable by a computer system and encoding a computer program ofinstructions for executing a computer process. The computer programproduct may also be a propagated signal on a carrier readable by acomputing system and encoding a computer program of instructions forexecuting a computer process. Accordingly, the present disclosure may beembodied in hardware and/or in software (including firmware, residentsoftware, micro-code, etc.). In other words, embodiments of the presentdisclosure may take the form of a computer program product on acomputer-usable or computer-readable storage medium havingcomputer-usable or computer-readable program code embodied in the mediumfor use by or in connection with an instruction execution system. Acomputer-usable or computer-readable medium may be any medium that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. More specific computer-readable medium examples (anon-exhaustive list), the computer-readable medium may include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, and a portable compact disc read-only memory(CD-ROM). Note that the computer-usable or computer-readable mediumcould even be paper or another suitable medium upon which the program isprinted, as the program can be electronically captured, via, forinstance, optical scanning of the paper or other medium, then compiled,interpreted, or otherwise processed in a suitable manner, if necessary,and then stored in a computer memory.

While certain embodiments of the disclosure have been described, otherembodiments may exist. Furthermore, although embodiments of the presentdisclosure have been described as being associated with data stored inmemory and other storage mediums, data can also be stored on or readfrom other types of computer-readable media, such as secondary storagedevices, like hard disks, floppy disks, or a CD-ROM, a carrier wave fromthe Internet, or other forms of RAM or ROM. Further, the disclosedmethods' stages may be modified in any manner, including by reorderingstages and/or inserting or deleting stages, without departing from thedisclosure.

Furthermore, embodiments of the disclosure may be practiced in anelectrical circuit comprising discrete electronic elements, packaged orintegrated electronic chips containing logic gates, a circuit utilizinga microprocessor, or on a single chip containing electronic elements ormicroprocessors. Embodiments of the disclosure may also be practicedusing other technologies capable of performing logical operations suchas, for example, AND, OR, and NOT, including but not limited to,mechanical, optical, fluidic, and quantum technologies. In addition,embodiments of the disclosure may be practiced within a general purposecomputer or in any other circuits or systems.

Embodiments of the disclosure may be practiced via a system-on-a-chip(SOC) where each or many of the element illustrated in FIG. 1 may beintegrated onto a single integrated circuit. Such an SOC device mayinclude one or more processing units, graphics units, communicationsunits, system virtualization units and various application functionalityall of which may be integrated (or “burned”) onto the chip substrate asa single integrated circuit. When operating via an SOC, thefunctionality described herein with respect to embodiments of thedisclosure, may be performed via application-specific logic integratedwith other components of computing device 600 on the single integratedcircuit (chip).

Embodiments of the present disclosure, for example, are described abovewith reference to block diagrams and/or operational illustrations ofmethods, systems, and computer program products according to embodimentsof the disclosure. The functions/acts noted in the blocks may occur outof the order as shown in any flowchart. For example, two blocks shown insuccession may in fact be executed substantially concurrently or theblocks may sometimes be executed in the reverse order, depending uponthe functionality/acts involved.

While the specification includes examples, the disclosure's scope isindicated by the following claims. Furthermore, while the specificationhas been described in language specific to structural features and/ormethodological acts, the claims are not limited to the features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example for embodiments of the disclosure.

What is claimed is:
 1. A method comprising: receiving, by a client device, at least a portion of failure prediction data; analyzing, by the client device, the at least the portion of the failure prediction data; and roaming, by the client device, from a first computing device to a second computing device in response to analyzing the at least the portion of the failure prediction data.
 2. The method of claim 1, further comprising: receiving, by the first computing device, the failure prediction data; and sending, by the first computing device in response to receiving the failure prediction data by the first computing device, the at least the portion of the failure prediction data to the client device.
 3. The method of claim 2, wherein sending the at least the portion of the failure prediction data to the client device comprises sending the at least the portion of the failure prediction data to the client device when the at least the portion of the failure prediction data indicates that a failure of the first computing device is likely to occur within a predetermined threshold.
 4. The method of claim 1, wherein the first computing device and the second computing device provide wireless access to a network.
 5. The method of claim 1, wherein the first computing device comprises a first Access Point (AP) and the second computing device comprises a second AP.
 6. The method of claim 1, wherein the failure prediction data includes at least one of a type of a predicted failure, a probability of the predicted failure, a time span until the predicted failure, and a degree of the predicted failure.
 7. The method of claim 1, wherein the client device performs a proactive action subsequent to analyzing the at least the portion of the failure prediction data and prior to roaming from the first computing device to the second computing device.
 8. The method of claim 7, wherein the proactive action comprises at least one of scanning for the second computing device, emptying the client device's buffer, switching to a blocks Modulation and Coding Scheme (MCS), switching to a more robust MCS, starting a backup Multi-Link Device (MLD) link, and trigging a network-layer multipath link.
 9. A method comprising: receiving, by a computing device, failure prediction data; analyzing, by the computing device, the failure prediction data; and sending, in response to analyzing the failure prediction data, a message to ones of a plurality of client devices to roam away from the computing device.
 10. The method of claim 9, wherein: analyzing the failure prediction data comprises determining that a probability that the computing device will fail is greater than a predetermined threshold; and sending the message to the ones of the plurality of client devices to roam away from the computing device comprises sending the message to all client devices attached to the computing device.
 11. The method of claim 9, wherein: analyzing the failure prediction data comprises determining that a probability that the computing device will fail is greater than a predetermined threshold; and sending the message to the ones of the plurality of client devices to roam away from the computing device comprises sending the message to the ones of the plurality of client devices in an order based on client category type.
 12. The method of claim 9, wherein: analyzing the failure prediction data comprises determining that a probability that the computing device will fail is greater than a predetermined threshold; and sending the message to the ones of the plurality of client devices to roam away from the computing device comprises sending the message to the ones of the plurality of client devices having a predetermined client category type.
 13. The method of claim 9, wherein the computing device comprises an Access Point (AP).
 14. A method comprising: receiving, by a client device, information comprising at least a portion of failure prediction data, a suggested action, and comparative elements on neighboring computing devices; analyzing, by the client device, the information; and taking an action by the client device in response to analyzing the information.
 15. The method of claim 14, further comprising: receiving, by a first computing device, the failure prediction data; and sending, in response to receiving the failure prediction data, the information to the client device.
 16. The method of claim 15, wherein taking the action comprises roaming, by the client device, from the first computing device to a second computing device wherein the second computing device comprises a one of the neighboring computing devices.
 17. The method of claim 14, wherein taking the action comprises switching, by the client device, to one of a Multi-Link Device (MLD) link, a 4G link, and a 5G link.
 18. A method comprising: receiving, by a computing device, failure prediction data; analyzing a configuration of a wireless network taking into account the failure prediction data; and reconfiguring, in response to analyzing the configuration of the wireless network, the wireless network.
 19. The method of claim 18, wherein reconfiguring the wireless network comprises removing an Access Point (AP) from the wireless network when analyzing the configuration of the wireless network taking into account the failure prediction data indicates the AP is likely to fail.
 20. The method of claim 18, wherein reconfiguring the wireless network comprises, when analyzing the configuration of the wireless network taking into account the failure prediction data indicates a Channel Utilization (CU) on an Access Point (AP) is greater than a predetermined threshold, at least one of increasing client count tolerance for the wireless network and causing the AP on the wireless network to refuse new associations. 